
import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			''''''
     lowerCAmelCase_							   =			(
         None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
     )
     lowerCAmelCase_							   =			None  # compression type in fsspec. ex: "gzip"
     lowerCAmelCase_							   =			None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz



     def __init__(				self							:							str     ,						_A							:							str = ""     ,						_A							:							Optional[str] = None     ,						_A							:							Optional[dict] = None     ,						**_A							:							List[Any] ):







       """simple docstring"""




       super().__init__(self     ,						**_A )
       # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
       __SCREAMING_SNAKE_CASE      :	Tuple					=		fsspec.open(
           _A     ,						mode='''rb'''     ,						protocol=_A     ,						compression=self.compression     ,						client_kwargs={
               '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
               '''trust_env''': True,  # Enable reading proxy env variables.
               **(target_options or {}).pop('''client_kwargs'''     ,						{} ),  # To avoid issues if it was already passed.
           }     ,						**(target_options or {})     ,						)
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		os.path.basename(self.file.path.split('''::''' )[0] )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(
           self.compressed_name[: self.compressed_name.rindex('''.''' )]
           if '''.''' in self.compressed_name
           else self.compressed_name
       )
       __SCREAMING_SNAKE_CASE      :	Any					=		None



     @classmethod
     def  UpperCAmelCase__							(				cls							:							List[str]     ,						_A							:							Optional[int] ):







       """simple docstring"""




       return super()._strip_protocol(_A ).lstrip('''/''' )



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       if self.dir_cache is None:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		{**self.file.fs.info(self.file.path ), '''name''': self.uncompressed_name}
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{f['''name''']: f}



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							str ):







       """simple docstring"""




       return self.file.open().read()




     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							str     ,						_A							:							str = "rb"     ,						_A							:							Tuple=None     ,						_A							:							Union[str, Any]=True     ,						_A							:							Dict=None     ,						**_A							:							List[Any]     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		self._strip_protocol(_A )
       if mode != "rb":
         raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''' )
       return self.file.open()






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''bz2'''
     lowerCAmelCase_							   =			'''bz2'''
     lowerCAmelCase_							   =			'''.bz2'''






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''gzip'''
     lowerCAmelCase_							   =			'''gzip'''
     lowerCAmelCase_							   =			'''.gz'''






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''lz4'''
     lowerCAmelCase_							   =			'''lz4'''
     lowerCAmelCase_							   =			'''.lz4'''






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''xz'''
     lowerCAmelCase_							   =			'''xz'''
     lowerCAmelCase_							   =			'''.xz'''






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''zstd'''
     lowerCAmelCase_							   =			'''zstd'''
     lowerCAmelCase_							   =			'''.zst'''



     def __init__(				self							:							str     ,						_A							:							str     ,						_A							:							str = "rb"     ,						_A							:							Optional[str] = None     ,						_A							:							Optional[dict] = None     ,						_A							:							int = DEFAULT_BLOCK_SIZE     ,						**_A							:							str     ,						):







       """simple docstring"""




       super().__init__(
           fo=_A     ,						mode=_A     ,						target_protocol=_A     ,						target_options=_A     ,						block_size=_A     ,						**_A     ,						)
       # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
       #
       # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
       #     out.close = close
       # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
       #
       # see https://github.com/intake/filesystem_spec/issues/725
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.file.__enter__





       class     __UpperCamelCase :






            """simple docstring"""







            def __init__(				self							:							int     ,						_A							:							Optional[Any] ):







              """simple docstring"""




              __SCREAMING_SNAKE_CASE      :	Tuple					=		file_



            def __enter__(				self							:							int ):







              """simple docstring"""




              self._file.__enter__()
              return self



            def __exit__(				self							:							Any     ,						*_A							:							int     ,						**_A							:							List[Any] ):







              """simple docstring"""




              self._file.__exit__(*_A     ,						**_A )



            def __iter__(				self							:							List[Any] ):







              """simple docstring"""




              return iter(self._file )



            def  UpperCAmelCase__							(				self							:							int ):







              """simple docstring"""




              return next(self._file )




            def __getattr__(				self							:							Dict     ,						_A							:							Optional[int] ):







              """simple docstring"""




              return getattr(self._file     ,						_A )

       def fixed_enter(*_A							:							List[str]     ,						**_A							:							str ):
         return WrappedFile(_enter(*_A     ,						**_A ) )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		fixed_enter

"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  List[Any]    				=			StableDiffusionLDMaDPipeline
				A__ :  List[str]    				=			TEXT_TO_IMAGE_PARAMS
				A__ :  str    				=			TEXT_TO_IMAGE_BATCH_PARAMS
				A__ :  Tuple    				=			TEXT_TO_IMAGE_IMAGE_PARAMS

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,					cross_attention_dim=32      ,					)
										lowerCamelCase__   =DDIMScheduler(
										    beta_start=0.0_0_0_8_5      ,					beta_end=0.0_1_2      ,					beta_schedule="scaled_linear"      ,					clip_sample=_lowerCamelCase      ,					set_alpha_to_one=_lowerCamelCase      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =AutoencoderKL(
										    block_out_channels=[32, 64]      ,					in_channels=6      ,					out_channels=6      ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,					latent_channels=4      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =CLIPTextConfig(
										    bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-05      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1000      ,					)
										lowerCamelCase__   =CLIPTextModel(_lowerCamelCase )
										lowerCamelCase__   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

										lowerCamelCase__   ={
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0 ):
										if str(_lowerCamelCase ).startswith("mps" ):
																lowerCamelCase__   =torch.manual_seed(_lowerCamelCase )
										else:
																lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator

										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
										lowerCamelCase__   =np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

										assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs["prompt"]]

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs.pop("prompt" )]

										lowerCamelCase__   =ldmad_pipe.tokenizer(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=ldmad_pipe.tokenizer.model_max_length      ,					truncation=_lowerCamelCase      ,					return_tensors="pt"      ,					)
										lowerCamelCase__   =text_inputs["input_ids"].to(_lowerCamelCase )

										lowerCamelCase__   =ldmad_pipe.text_encoder(_lowerCamelCase )[0]

										lowerCamelCase__   =prompt_embeds

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
										assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4






				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =PNDMScheduler(skip_prk_steps=_lowerCamelCase )
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   ="french fries"
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase      ,					negative_prompt=_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
										lowerCamelCase__   =np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
										assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2






@slow
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1].flatten()
										lowerCamelCase__   =rgb[0, -3:, -1].flatten()

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512)

										lowerCamelCase__   =np.array(
										    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
										lowerCamelCase__   =np.array(
										    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
										assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
										assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3






@nightly
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 50,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_9_5_5_8_6
										lowerCamelCase__   =0.3_3_7_9_5_5_1_5
										lowerCamelCase__   =1_1_2.4_8_5_1_8
										lowerCamelCase__   =9_8.4_8_9_7_4_6
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_1_9_4_1_2_7
										lowerCamelCase__   =0.3_5_3_7_5_5_8_6
										lowerCamelCase__   =0.5_6_3_8_5_0_2
										lowerCamelCase__   =0.3_4_6_8_6_1_0_3

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512, 1)
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3


